1. Field of the Invention
The present inventions relates to intelligent control systems, and in particular, to intelligent controllers using neural network-based fuzzy logic.
2. Description of the Related Art
Fuzzy logic offers a number of significant advantages over conventional design approaches for non-linear systems. For example, non-linear and time-variant systems which are poorly understood or are difficult to model or implement in a cost effective manner can be designed using fuzzy logic. Further, fuzzy logic offers cost effective, as well as robust, solutions. However, in conventional fuzzy logic design, the shaping of the membership functions and the generating of an optimal number of appropriate rules often proves to be quite difficult. Further, as the complexity of the design increases, such difficulty in producing a conventional fuzzy logic solution increases dramatically. Accordingly, conventional fuzzy logic design has embraced the use of neural networks, resulting in the development of "neural-fuzzy" technology.
In neural-fuzzy technology, a neural network is used to generate the fuzzy logic rules and membership functions of the system. Sample input/output patterns are used as the input to the neural network. The network learns based upon these patterns and generates appropriate rules and membership functions. Since the neural network is highly computation intensive, the equivalent fuzzy logic model (obtained from a direct mapping of the neural network into a fuzzy logic model) provides a more cost effective solution, as compared to the neural network solution by itself.
However, a major drawback of the neural-fuzzy design approach is that the accuracy of the solution is limited by the accuracy of the training data. Accurate training data is quite difficult to obtain and inaccuracies in the training data are reflected in the fuzzy logic solution produced by the neural network. In a worst case situation, the neural network may never converge to a desired level of accuracy because of discontinuities in corrupted training data. In such cases, some measures may need to be taken to improve the discontinuities in the training data. However, some discrepancies may still remain and so the accuracy of the actual fuzzy system may not be adequate, although the fuzzy logic accuracy would map well to neural network accuracy.